Staff Software Engineer - Infrastructure Storage
Lambda· Data Center Business
Hybrid (San Francisco Office (Fremont St)), Hybrid (San Jose Office (First St)), Hybrid (Bellevue Office)$349k - $465kFull-timePosted 3mo ago
About the role
Seasoned Staff Storage Software Engineer focused on designing and deploying storage protocol solutions at scale across object, block, and file paradigms within Lambda’s AI infrastructure platform.
Responsibilities
- Technical Leadership: Set technical direction for storage software architecture across petabyte-scale deployments, authoring and reviewing design docs, mentoring senior engineers, and serving as the technical anchor for cross-functional initiatives spanning storage, networking, compute, and control plane teams. Represent the storage software team in architectural reviews, roadmap planning, and customer-facing technical discussions.
- Execution: Design, develop, and maintain high-performance storage systems software across file (NFS, SMB, Lustre), block (NVMe-oF, iSCSI), and object (S3) protocols. Build distributed systems for orchestrating storage resources, integrate with NVMe/GPU-direct/DPU-accelerated hardware, and troubleshoot complex production issues across performance, protocol, and hardware failure domains. Own the full lifecycle from requirements and design through deployment, monitoring, and maintenance, including benchmarking, profiling, and capacity planning tooling.
- Collaboration: Partner with storage software, networking, control plane, Kubernetes, observability, compute, and fleet engineering teams to deliver cross-functional infrastructure initiatives, define and track storage SLOs/SLIs, and ensure reliable deployment and maintenance of distributed storage infrastructure.
- Innovate: Stay current with AI and HPC storage research, evaluate emerging protocols and hardware, and optimize solutions for AI workloads including checkpoint I/O, high-throughput dataset serving, and latency-sensitive inference pipelines.
Qualifications
- 10+ years in storage systems engineering, with 5+ years in a technical lead or Staff+ IC role. Proven track record designing and operating multi-petabyte storage infrastructure in production data center or cloud environments. Background in HPC, AI/ML infrastructure, or large-scale cloud storage.
- Systems-Level Programming: Strong proficiency in C, C++, Rust, or Go. Ability to write high-performance, concurrent, production-grade systems code. Familiarity with DPDK/SPDK and kernel-bypass data paths is a plus; kernel-level storage driver or storage daemon experience is even better.
- Storage Protocol & API Expertise: Deep hands-on experience with two or more protocols, object (S3), block (iSCSI, NVMe-oF), or file (NFS, SMB, Lustre, DAOS), including implementing or maintaining protocol servers/clients in production, not just consuming them.
- Storage Performance Optimization: Experience profiling and tuning for throughput, latency, and IOPS under real workloads using tools like fio, elbencho.
- Modern Storage Technologies: Working knowledge of NVMe, NVMe-oF, RDMA (RoCE or InfiniBand), and DPUs (e.g., NVIDIA BlueField).
- Operational Acumen: Comfortable in physical data center environments, rack-scale infrastructure, storage hardware, failure domains. Experience designing for reliability, writing runbooks, and driving incident response. Familiar with storage observability tooling (Prometheus, Grafana, log aggregation, tracing).
Skills
Required:Storage software architectureSystems programmingStorage protocols
Benefits
- Health, dental, and vision coverage for you and your dependents
- Wellness and commuter stipends for select roles
- 401k Plan with 2% company match (USA employees)
- Flexible paid time off plan that we all actually use
Last updated Jul 23, 2026. Interested? Apply on the company site.
About Lambda
The Superintelligence Cloud
The gigawatt-scale AI GPU Cloud built for superintelligence. Featuring on-demand & reserved cloud NVIDIA HGX B200, B300, GB200, GB300 and H200 GPUs for AI training & inference.
AI trainingDeep learningGPU CloudInferenceNVIDIA GPUsliquid-ai-recruiting-targetsetter-30setter-30-2026-q1
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